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Tutorial 2: Adding New Dataset

Customize datasets by reorganizing data to COCO format

The simplest way to use the custom dataset is to convert your annotation format to COCO dataset format.

The annotation json files in COCO format has the following necessary keys:

'images': [
    {
        'file_name': '000000001268.jpg',
        'height': 427,
        'width': 640,
        'id': 1268
    },
    ...
],
'annotations': [
    {
        'segmentation': [[426.36,
            ...
            424.34,
            223.3]],
        'keypoints': [0,0,0,
            0,0,0,
            0,0,0,
            427,220,2,
            443,222,2,
            414,228,2,
            449,232,2,
            408,248,1,
            454,261,2,
            0,0,0,
            0,0,0,
            411,287,2,
            431,287,2,
            0,0,0,
            458,265,2,
            0,0,0,
            466,300,1],
        'num_keypoints': 10,
        'area': 3894.5826,
        'iscrowd': 0,
        'image_id': 1268,
        'bbox': [402.34, 205.02, 65.26, 88.45],
        'category_id': 1,
        'id': 215218
    },
    ...
],
'categories': [
    {'id': 1, 'name': 'person'},
 ]

There are three necessary keys in the json file:

  • images: contains a list of images with their information like file_name, height, width, and id.

  • annotations: contains the list of instance annotations.

  • categories: contains the category name (‘person’) and its ID (1).

Create a custom dataset_info config file for the dataset

Add a new dataset info config file.

configs/_base_/datasets/custom.py

An example of the dataset config is as follows.

keypoint_info contains the information about each keypoint.

  1. name: the keypoint name. The keypoint name must be unique.

  2. id: the keypoint id.

  3. color: ([B, G, R]) is used for keypoint visualization.

  4. type: ‘upper’ or ‘lower’, will be used in data augmetation.

  5. swap: indicates the ‘swap pair’ (also known as ‘flip pair’). When applying image horizontal flip, the left part will become the right part. We need to flip the keypoints accordingly.

skeleton_info contains the information about the keypoint connectivity, which is used for visualization.

joint_weights assigns different loss weights to different keypoints.

sigmas is used to calculate the OKS score. Please read keypoints-eval to learn more about it.

dataset_info = dict(
    dataset_name='coco',
    paper_info=dict(
        author='Lin, Tsung-Yi and Maire, Michael and '
        'Belongie, Serge and Hays, James and '
        'Perona, Pietro and Ramanan, Deva and '
        r'Doll{\'a}r, Piotr and Zitnick, C Lawrence',
        title='Microsoft coco: Common objects in context',
        container='European conference on computer vision',
        year='2014',
        homepage='http://cocodataset.org/',
    ),
    keypoint_info={
        0:
        dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),
        1:
        dict(
            name='left_eye',
            id=1,
            color=[51, 153, 255],
            type='upper',
            swap='right_eye'),
        2:
        dict(
            name='right_eye',
            id=2,
            color=[51, 153, 255],
            type='upper',
            swap='left_eye'),
        3:
        dict(
            name='left_ear',
            id=3,
            color=[51, 153, 255],
            type='upper',
            swap='right_ear'),
        4:
        dict(
            name='right_ear',
            id=4,
            color=[51, 153, 255],
            type='upper',
            swap='left_ear'),
        5:
        dict(
            name='left_shoulder',
            id=5,
            color=[0, 255, 0],
            type='upper',
            swap='right_shoulder'),
        6:
        dict(
            name='right_shoulder',
            id=6,
            color=[255, 128, 0],
            type='upper',
            swap='left_shoulder'),
        7:
        dict(
            name='left_elbow',
            id=7,
            color=[0, 255, 0],
            type='upper',
            swap='right_elbow'),
        8:
        dict(
            name='right_elbow',
            id=8,
            color=[255, 128, 0],
            type='upper',
            swap='left_elbow'),
        9:
        dict(
            name='left_wrist',
            id=9,
            color=[0, 255, 0],
            type='upper',
            swap='right_wrist'),
        10:
        dict(
            name='right_wrist',
            id=10,
            color=[255, 128, 0],
            type='upper',
            swap='left_wrist'),
        11:
        dict(
            name='left_hip',
            id=11,
            color=[0, 255, 0],
            type='lower',
            swap='right_hip'),
        12:
        dict(
            name='right_hip',
            id=12,
            color=[255, 128, 0],
            type='lower',
            swap='left_hip'),
        13:
        dict(
            name='left_knee',
            id=13,
            color=[0, 255, 0],
            type='lower',
            swap='right_knee'),
        14:
        dict(
            name='right_knee',
            id=14,
            color=[255, 128, 0],
            type='lower',
            swap='left_knee'),
        15:
        dict(
            name='left_ankle',
            id=15,
            color=[0, 255, 0],
            type='lower',
            swap='right_ankle'),
        16:
        dict(
            name='right_ankle',
            id=16,
            color=[255, 128, 0],
            type='lower',
            swap='left_ankle')
    },
    skeleton_info={
        0:
        dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
        1:
        dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
        2:
        dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
        3:
        dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
        4:
        dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
        5:
        dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
        6:
        dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
        7:
        dict(
            link=('left_shoulder', 'right_shoulder'),
            id=7,
            color=[51, 153, 255]),
        8:
        dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
        9:
        dict(
            link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
        10:
        dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
        11:
        dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
        12:
        dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]),
        13:
        dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),
        14:
        dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),
        15:
        dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]),
        16:
        dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]),
        17:
        dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]),
        18:
        dict(
            link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255])
    },
    joint_weights=[
        1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
        1.5
    ],
    sigmas=[
        0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
        0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
    ])

Create a custom dataset class

  1. First create a package inside the mmpose/datasets/datasets folder.

  2. Create a class definition of your dataset in the package folder and register it in the registry with a name. Without a name, it will keep giving the error. KeyError: 'XXXXX is not in the dataset registry'

    @DATASETS.register_module(name='MyCustomDataset')
    class MyCustomDataset(SomeOtherBaseClassAsPerYourNeed):
    
  3. Make sure you have updated the __init__.py of your package folder

  4. Make sure you have updated the __init__.py of the dataset package folder.

Create a custom training config file

Create a custom training config file as per your need and the model/architecture you want to use in the configs folder. You may modify an existing config file to use the new custom dataset.

In configs/my_custom_config.py:

...
# dataset settings
dataset_type = 'MyCustomDataset'
...
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        ann_file='path/to/your/train/json',
        img_prefix='path/to/your/train/img',
        ...),
    val=dict(
        type=dataset_type,
        ann_file='path/to/your/val/json',
        img_prefix='path/to/your/val/img',
        ...),
    test=dict(
        type=dataset_type,
        ann_file='path/to/your/test/json',
        img_prefix='path/to/your/test/img',
        ...))
...

Make sure you have provided all the paths correctly.

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